New Bayesian analysis features

The most requested additions for Bayesian analysis — multiple chains and Bayesian predictions—are now available. You can use multiple chains with Bayesian estimation to evaluate MCMC convergence. And you can now evaluate convergence using the Gelman–Rubin convergence diagnostic. With Bayesian predictions, you can check model fit and predict out-of-sample observations.

Stata 16 offers extensive additions to Stata's Bayesian suite of commands,
which include

Multiple chains

Gelman–Rubin convergence diagnostics

Bayesian predictions

Posterior summaries of simulated values

MCMC replicate

Posterior predictive p-values

In addition, bayes: and bayesmh support new priors
pareto(), dirichlet(), and geometric() for specifying, respectively, Pareto, multivariate beta (Dirichlet), and geometric prior distributions. Pareto is a power-law-based distribution. Dirichlet can be used for
specifying priors for probability vector parameters. Geometric priors are
suitable for modeling count parameters.